Electronic apparatus and controlling method thereof

ABSTRACT

Disclosed is an electronic apparatus. The electronic apparatus includes a processor configured to obtain first upscaling information of an input image using an artificial intelligence (AI) model that is trained to obtain upscaling information of an image. The processor is also configured to downscale the input image based on the obtained first upscaling information, and obtain an output image by upscaling the downscaled image based on an output resolution.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based on and claims priority under 35 U.S.C. § 119from Korean Patent Application No. 10-2019-0156162, filed on Nov. 28,2019, in the Korean Intellectual Property Office, the disclosure ofwhich is incorporated by reference herein in its entirety.

BACKGROUND 1. Field

The disclosure relates to an electronic apparatus and a method forcontrolling thereof. More particularly, the disclosure relates to anelectronic apparatus performing image processing using an artificialintelligence (AI) model and a method for controlling thereof.

2. Description of Related Art

The development of electronic technology has enabled development anddissemination of various types of electronic apparatuses. In particular,display devices used in various places, such as homes, offices, publicplaces, and the like, have recently been developed for recent years.

Recently, a demand for high-resolution image services has been greatlyincreasing. Accordingly, as the distribution of a display device such asa television (TV) capable of supporting a high-resolution image isextended, there are increasing cases where upscaling of an image isperformed in various environments, such as a set-top box (STB), a publicTV, etc., and the upscaled image is transmitted to a display device suchas a TV.

However, according to various upscaling techniques and broadcastingenvironments, the upscaled images have different image qualities andthus show poor performance compared to the upscaling technique providedin a TV.

SUMMARY

Various example embodiments of the disclosure address the abovedisadvantages and other disadvantages not described above.

Provided herein is an electronic apparatus including: a processorconfigured to perform operations including: obtaining first upscalinginformation of an input image by using a first artificial intelligence(AI) model, wherein the first AI model is trained to obtain upscalinginformation, and performing one of: determining not to downscale theinput image, or obtaining a downscaled image by downscaling, based onthe first upscaling information, the input image, and obtaining, basedon an output resolution, an output image by upscaling the downscaledimage.

Also provided herein is a method of controlling an electronic apparatus,the method including: obtaining first upscaling information of an inputimage by using a first artificial intelligence (AI) model, wherein thefirst AI model is trained to obtain upscaling information; andperforming one of: determining not to downscale the input image, orobtaining a downscaled image by downscaling, based on the firstupscaling information, the input image, and obtaining, based on anoutput resolution, an output image by upscaling the downscaled image.

An electronic apparatus according to an embodiment includes a processorconfigured to obtain first upscaling information of an input image usinga first artificial intelligence (AI) model that is trained to obtainupscaling information of an image, downscale the input image based onthe obtained first upscaling information, and obtain an output image byupscaling the downscaled image based on an output resolution.

A method of controlling an electronic apparatus according to anembodiment may include obtaining first upscaling information of an inputimage using a first artificial intelligence (AI) model that is trainedto obtain upscaling information of an image; downscaling the input imagebased on the obtained first upscaling information; and obtaining anoutput image by upscaling the downscaled image based on an outputresolution.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects, features, and advantages of certainembodiments of the disclosure will be more apparent from the followingdetailed description, taken in conjunction with the accompanyingdrawings, in which:

FIG. 1A is a view to describe an image quality processing operation ofan electronic apparatus to help understanding of the disclosure;

FIG. 1B is a view illustrating example operations according to anembodiment;

FIGS. 2A and 2B are block diagrams illustrating a configuration of anelectronic apparatus according to an embodiment;

FIG. 3 is a flowchart to describe an operation of an electronicapparatus according to an embodiment;

FIG. 4A is a view to describe a training method of a first AI modelaccording to an embodiment;

FIG. 4B is a view to describe a training method of a first AI modelaccording to an embodiment;

FIG. 5 is a view to describe a method for obtaining feature informationinput to the first AI model according to an embodiment;

FIG. 6A is a view to describe blurring according to an embodiment;

FIG. 6B is a view to describe blurring according to an embodiment;

FIG. 7A is a view to describe a method for obtaining a feature valueaccording to an embodiment;

FIG. 7B is a view to describe a method for obtaining a feature valueaccording to an embodiment;

FIG. 7C illustrates an example related to feature information;

FIG. 8 is a view to describe a method of scene-based downscalingaccording to an embodiment;

FIG. 9A is a view to describe a structure of the first AI modelaccording to an embodiment;

FIG. 9B is a view to describe a structure of the first AI modelaccording to an embodiment;

FIG. 10A is a view to describe a method for obtaining training data fortraining the first AI model according to an embodiment;

FIG. 10B is a view to describe a method for obtaining training data fortraining the first AI model according to an embodiment;

FIG. 10C is a view to describe a method for obtaining training data fortraining the first AI model according to an embodiment;

FIG. 10D is a view to describe a method for obtaining training data fortraining the first AI model according to an embodiment;

FIG. 10E is a view to describe a method for obtaining training data fortraining the first AI model according to an embodiment;

FIG. 10F is a view to describe a method for obtaining training data fortraining the first AI model according to an embodiment;

FIG. 11A is a view to describe a method for upscaling processing usingthe second AI model according to an embodiment;

FIG. 11B is a view to describe a method for upscaling processing usingthe second AI model according to an embodiment;

FIG. 11C is a view to describe a method for upscaling processing usingthe second AI model according to an embodiment;

FIG. 12 is a view to describe an operation of an electronic apparatusaccording to an embodiment;

FIG. 13 is a view to describe a modified embodiment of an operation ofthe electronic apparatus illustrated in FIG. 12;

FIG. 14 is a view to describe an image analysis operation illustrated inFIGS. 12 and 13 in a greater detail;

FIG. 15 is a view to describe a hardware structure of an electronicapparatus according to an embodiment;

FIG. 16 is a view illustrating an embodiment of an electronic apparatusaccording to an embodiment; and

FIG. 17 is a flowchart to describe a method for controlling anelectronic apparatus according to an embodiment.

DETAILED DESCRIPTION

The disclosure provides an electronic apparatus providing ahigh-resolution image by estimating an original resolution of an imageupscaled from the outside, downscaling the image, and upscaling thedownscaled image using a high-performance upscaling technology and amethod for controlling thereof.

The disclosure will be further described with reference to the attacheddrawings.

The terms used in this specification will be briefly described, and thedisclosure will be described in greater detail.

General terms that are currently widely used were selected as terms usedin embodiments of the disclosure in consideration of functions in thedisclosure, but may be changed depending on the intention of thoseskilled in the art or a judicial precedent, the emergence of a newtechnique, and the like. In addition, in a specific case, termsarbitrarily chosen by an applicant may exist. In this case, the meaningof such terms will be mentioned in detail in a corresponding descriptionportion of the disclosure. Therefore, the terms used in embodiments ofthe disclosure should be defined on the basis of the contents throughoutthe disclosure rather than simple names of the terms.

The terms such as “first,” “second,” and so on may be used to describe avariety of elements, but the elements should not be limited by theseterms. The terms are used only for the purpose of distinguishing oneelement from another.

A singular expression includes a plural expression, unless otherwisespecified. It is to be understood that the terms such as “comprise” or“consist of” are used herein to designate a presence of acharacteristic, number, step, operation, element, component, or acombination thereof, and not to preclude a presence or a possibility ofadding one or more of other characteristics, numbers, steps, operations,elements, components or a combination thereof.

It should be understood that at least one of A or B indicates “A”, “B”or one of “A and B”.

The term such as “module,” “unit,” “part”, and so on is used to refer toan element that performs at least one function or operation, and suchelement may be implemented as hardware or software, or a combination ofhardware and software. Further, except for when each of a plurality of“modules”, “units”, “parts”, and the like needs to be realized in anindividual hardware, the components may be integrated in at least onemodule or chip and be realized in at least one processor (not shown).

Hereinafter, embodiments will be described in detail with reference tothe accompanying drawings so that those skilled in the art may easilycarry out the embodiment. However, the disclosure may be embodied inmany different forms and is not limited to the embodiments describedherein. In order to clearly illustrate the disclosure in the drawings,portions which are not related to the description have been omitted, andlike reference numerals have been assigned to similar portionsthroughout the specification.

FIG. 1A is a view to describe an image quality processing operation ofan electronic apparatus to help understanding of the disclosure.

According to the image quality processing operation of the electronicapparatus illustrated in FIG. 1A, different image quality processingpaths are applied based on the resolution of an input image. Here, theresolution of the input image may include standard definition (SD) of720×480, high definition (HD) of 1280×720, full high definition (FHD) of1920×1080, quad high definition (QHD) of 2560×1440, 4K ultra highdefinition (UHD) of 3840×2160, 8K ultra high definition (UHD) of7680×4320, and higher resolutions (e.g., 16K, 32K).

According to an embodiment, if an image 11 below a threshold resolutionis input, the electronic apparatus may perform first image qualityprocessing 21 at the input resolution level and then upscale 30 theimage processed with first image quality processing to an outputresolution, for example, a maximum output resolution, and obtain anoutput image by performing a second image quality processing 40 for theupscaled image. However, if an image 12 having a resolution greater thanor equal to a threshold resolution is input, the electronic apparatusmay obtain an output image by performing the first image qualityprocessing 22 for the image, and then processing the second imagequality processing 40 for the image processed with the first imagequality processing.

Here, the threshold resolution may be determined based on the maximumoutput resolution of the electronic apparatus. For example, if theelectronic apparatus is implemented with a TV in which 4K UHD is themaximum output resolution, the threshold resolution may be determined tobe FHD or QHD. For example, as for a TV in which the 4K UHD is themaximum output resolution, when an FHD image is input, the TV mayupscale the image to the 4K UHD image, but may not perform a separateupscaling process when the 4K UHD image is input.

That is, according to the embodiment described above, the electronicapparatus may not perform a separate upscaling processing for the image12 having a resolution greater than or equal to the thresholdresolution. For example, if an input image is an image having an outputresolution, a separate upscaling process for output is not required, anda high-cost calculator must be used for high-speed operation of hardware(e.g., an application specific integrated circuit (ASIC)) to improve theimage quality of a high-resolution image, and an internal buffer havinga relatively large capacity may be required. For example, since a linebuffer equal to the resolution of the input image must be implemented,for example, in the case of 4K UHD input image, a line buffer of3840/720=5.33 times longer than the SD input image may be required, andin the case of 8K UHD input image, a line buffer of 7680/720=10.66 timeslonger than the SD input image may be required. Image qualityimprovement intellectual property (IP), that is, the function blocks toimprove the image quality, is designed to output the optimal imagequality when the image of the original resolution is input, and theimage quality register values have been set already, the image isalready upscaled. In this case, the optimal image quality cannot beguaranteed for the image which has been already upscaled. Accordingly,when a low-quality image of a threshold resolution or greater (e.g., 4Kor 8K resolution) generated by a general upscaling technique in abroadcasting station or an external device such as a set-top box isinput to the electronic apparatus, functional block for scalingprocessing 30 needs not be gone through and thus, it is not avoidable toprovide a low-quality output image.

In general, then, an upscaling technique applied before item 30 of FIG.1A is reached, causes a problem if the fact of the upscaling havingoccurred is ignored.

This disclosure detects whether an upscaling has occurred or not. Seebelow, FIG. 17 item S1710. If an upscaling has occurred, a quantity ofthe upscaling, similar in some examples to a change in resolution isdetected. See below, FIG. 9A and the outputs of the “softmax,” also seeFIG. 9B and the two kinds of quantities outputs from items 921 and 922.This detection can occur repeatedly as scenes change, see below FIG. 8“upscale ratio,” “probability,” and “Final (showing 4K or UP4K).”

It will be described various embodiments wherein an output image with ahigh resolution is provided using functional blocks for upscalingprocessing 30 implemented in the electronic apparatus, even when animage having a resolution greater than or equal to a thresholdresolution is input to an electronic apparatus.

FIG. 1B illustrates an example embodiment. In FIG. 1B, one images 11 and12 of FIG. 1A and an image 13 are presented to an Image Analysis Module91. Image analysis module 91 only analyzes and routes images, it doesnot transform or change an input image content or representation.

Image 12 emerges from Image Analysis Module 91 and passes to the output,because it is already high resolution and has not undergone an upscalingpreviously. In an example shown below in FIG. 12, an example of image 12is the input signal in the upper left of FIG. 12, labelled HD/FHD/UHDwhich emerges as UHD(ORIGINAL) in the upper right of FIG. 12(corresponding to image 94 of FIG. 1B).

Image 13 is low resolution, for example SD, and passes as image 96 to anthe upscaling 30. The upscaled version 96 then passes to post-processing40. An image 100 corresponds to image 98 after post-processing. In theexample shown below in FIG. 12, an example of image 13 is the inputsignal in the lower left of FIG. 12, labelled SD.

Image 11 is a high resolution image which has undergone upscalingpreviously, for example, by a set top box (STB) or at a broadcaster.Image 11 emerges from Image Analysis Module 91 as image 95 and enterspost-processing 40. Image 99, output from the Post-Processing 40,corresponds to image 97, which corresponds to image 10, whichcorresponds to image 95, which corresponds to image 11. In the exampleshown below in FIG. 12, an example of image 11 is the input signal inthe upper left of FIG. 12, labelled HD/FHD/UHD which emerges asHD/FHD/UHD in the middle of FIG. 12 (corresponding to image 95 of FIG.1B) along with scaling ratio 93.

By the operations of the modules illustrated in FIG. 1B, image 11 isrecognized as being upsampled, but possibly not of good quality,processed, and then output with an improved quality.

FIGS. 2A and 2B are block diagrams illustrating a configuration of anelectronic apparatus according to an embodiment.

Referring to FIG. 2A, an electronic apparatus 100′ includes a processor110, and referring to FIG. 2B, the electronic apparatus 100 includes theprocessor 110 and a memory 120. The information on the AI modelaccording to an embodiment may be stored in the internal memory of theprocessor 110, external memory, that is, the memory 120 and thus, FIG.2A and FIG. 2B have been illustrated separately. Hereinbelow, FIG. 2Bwill be described.

The electronic apparatus 100 may be implemented as a TV or a set-topbox, but is not limited thereto, and may be applicable to any devicehaving image processing and/or display function such as a smart phone, atablet PC, a notebook PC, a head mounted display (HMD), a near-eyedisplay (NED), a large format display (LFD), a digital signage, adigital information display (DID), a video wall, a projector display, acamera, a camcorder, a printer, a server, or the like. Alternatively,the electronic apparatus 100 may be a system itself in which cloudingcomputing environment is established. The embodiment is not limitedthereto, and is applicable to any device for processing data using theAI model.

According to an example, the electronic apparatus 100 may receive animage of various resolutions and various compressed images. For example,the electronic apparatus 100 may receive an image in at least one of astandard definition (SD), a high definition (HD), a full HD (FHD), anultra HD (UHD), higher than UHD resolutions. The electronic apparatus100 may receive an image in a compressed form such as a moving pictureexperts group (MPEG) (for example, MP2, MP4, MP7, etc.), jointphotographic coding experts group (JPEG), advanced video coding (AVC),H.264, H.265, a high efficiency video codec (HEVC), VC-1, VP8, VP9,AOMedia Video 1 (AV1) or the like.

The memory 120 may store necessary data for various embodiments of thedisclosure. The memory 120 may be implemented as a memory embedded withthe electronic apparatus 100, or may be implemented as a detachablememory in the electronic apparatus 100, according to the data usagepurpose. For example, data for driving the electronic apparatus 100 maybe stored in a memory embedded in the electronic apparatus 100, and datafor an expanded function of the electronic apparatus 100 may be storedin the memory detachable to the electronic apparatus 100. A memoryembedded in the electronic apparatus 100 may be a volatile memory suchas a dynamic random access memory (DRAM), a static random access memory(SRAM), a synchronous dynamic random access memory (SDRAM), or anonvolatile memory (for example, one time programmable ROM (OTPROM),programmable ROM (PROM), erasable and programmable ROM (EPROM),electrically erasable and programmable ROM (EEPROM), mask ROM, flashROM, a flash memory (for example, NAND flash or NOR flash), a hard diskdrive or a solid state drive (SSD). In the case of a memory detachablymounted to the electronic apparatus 100, the memory may be implementedas a memory card (for example, a compact flash (CF), secure digital(SD), micro secure digital (micro-SD), mini secure digital (mini-SD),extreme digital (xD), multi-media card (MMC), etc.), an external memory(for example, a USB memory) connectable to the USB port, or the like.

According to an example, the memory 120 may store at least oneinstruction or a computer program including an instruction forcontrolling the electronic apparatus 100.

According to another example, the memory 120 may store information aboutan AI model that includes a plurality of layers. Here, storinginformation about the AI model may mean storing various informationrelated to the operation of the AI model, for example, information abouta plurality of layers included in the AI model, information aboutparameters (e.g., filter coefficients, bias, etc.) used in each of theplurality of layers, and the like. For example, the memory 120 may storeinformation about the first AI model trained to obtain the upscalinginformation of the input image according to one embodiment. The memory120 may store information about a second AI model trained to upscale theimage according to one embodiment. Here, the upscaling process mayinclude, for example, super resolution processing. However, if theprocessor 110 is implemented as AI model-only hardware, informationabout the AI model may be stored in an internal memory of the processor110.

According to a still another example, the memory 120 may store an imagereceived from an external device (for example, a source device), anexternal storage medium (for example, universal serial bus (USB)), anexternal server (for example, web hard), or the like. The image may be adigital moving image but is not limited thereto.

According to still another example, the memory 120 may store variousinformation for image processing, for example, noise reduction, detailenhancement, tone mapping, contrast enhancement, color enhancement, orframe rate conversion, algorithm, image quality parameter, or the like.The memory 120 may store a final output image generated by imageprocessing.

According to an embodiment, the memory 120 may be implemented as asingle memory for storing the data generated from various operations ofthe disclosure. According to another embodiment, the memory 120 maystore data of different types, respectively, or may be implemented toinclude a plurality of memories for storing each of the data indifferent stages.

In the above embodiment, it has been described that various data isstored in the external memory 120 of the processor 110 but at least apart of the data mentioned above may be stored in the internal memory ofthe processor 110 according to an embodiment of at least one of theelectronic apparatus 100 or the processor 110.

The processor 110 is electrically connected to the memory 120 andcontrols overall operations of the electronic apparatus 100. Theprocessor 110 may be composed of one or a plurality of processors. Theprocessor 110 may perform an operation of the electronic apparatus 100according to various embodiments by executing at least one instructionstored in the memory 120.

According to an embodiment, the processor 110 may include a digitalsignal processor (DSP) for processing a digital image signal, amicroprocessor, a graphics processing unit (GPU), an artificialintelligence (AI) processor, a neural processing unit (NPU), a timecontroller (TCON), or the like, but is not limited thereto. Theprocessor 110 may include, for example, and without limitation, one ormore of a central processing unit (CPU), a micro controller unit (MCU),a micro processing unit (MPU), a controller, an application processor(AP), a communication processor (CP), an Advanced Reduced instructionset computing (RISC) Machine (ARM) processor, or the like, or may bedefined as a corresponding term. The processor 110 may be implemented ina system on chip (SoC) type or a large scale integration (LSI) typewhich a processing algorithm is built therein or in an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA) type.

The processor 110 for executing the AI model according to one embodimentmay be implemented through the combination between a general purposeprocessor, such as a central processing unit (CPU), an applicationprocessor (AP), a digital signal processor (DSP), or the like, agraphics-only processor such as a graphics processing unit (GPU), avision processing unit (VPU), or an AI-only processor such as a neuralprocessing unit (NPU) with software. The processor 110 may control toprocess the input data according to a predefined operating rule storedin the memory 120 or AI model. Alternatively, if the processor 110 is adedicated processor (or an AI-dedicated processor), it may be designedwith a hardware structure specialized for the processing of a particularAI model. For example, hardware specific to the processing of aparticular AI model may be designed into a hardware chip, such as anapplication specific integrated circuit (ASIC), field-programmable gatearray (FPGA), or the like. When the processor 110 is implemented as adedicated processor, it may be implemented to include the memory forimplementing embodiments of the disclosure, or may be implemented toinclude a memory processing functionality for using an external memory.

The processor 110 processes the input data to obtain output data. Here,the input data may include at least one of text, images, or user voices.For example, the input data may be input via a communicator communicablewith an external device, a user inputter such as a keyboard or a touchpad, a camera, a microphone, or the like. The output data may varydepending on the type of AI model. For example, the output data may be aresolution-enhanced image, object-related information included withinthe image, a text corresponding to voice, or the like.

According to an example, the processor 110 obtains an output image byperforming image processing for the input image. Here, the input imageor the output image may include a still image, a plurality ofconsecutive still images (or frames), or a video. The image processingmay be a digital image processing including at least one of imageenhancement, image restoration, image transformation, image analysis,image understanding, or image compression. According to an example, ifthe input image is a compressed image, the processor 110 may decode thecompressed image and then perform image processing. According to oneembodiment, the processor 110 may perform image processing for the inputimage using an AI model. For example, the processor 110 may load and useAI model-related information stored in the memory 120, for example, anexternal memory, such as a dynamic random access memory (DRAM), to usethe AI model.

FIG. 3 is a flowchart to describe an operation of the processor 110according to an embodiment.

According to an embodiment, the processor 110 may obtain the upscalinginformation of the input image in operation S310, downscale the inputimage based on the obtained upscaling information in operation S320, andupscale the downscaled image to obtain an output image in operationS330. The upscaling information of the input image may include at leastone of upscaling ratio information of the input image or originalresolution information of the input image. The upscaling ratio of aninput image may mean, if the input image is obtained by upscaling anoriginal image, the corresponding upscaling ratio. However, since theupscaling ratio can be 1 in the case of non-upscaled image, theupscaling ratio is not a term that is necessarily applied to the imageon which the upscaling is performed. According to an embodiment, oneprocessor 110 may perform the operations of steps S310, S320, and S330,but at least some operations of at least some steps may be performed byat least one other processor.

According to an example, the processor 110 may obtain the upscalinginformation of the input image using a first AI model trained to obtainthe upscaling information of the image. However, the embodiment is notlimited thereto and it is also possible to obtain the upscalinginformation of the input image without using the first AI model. Forexample, the processor 110 may obtain the upscaling information of theinput image in a variety of ways based on the pixel information of theinput image. For example, the pixel value distribution around the edgeregion may be confirmed to identify the upscaling ratio or the originalresolution information. Herein, according to an embodiment, the first AImodel is used to obtain accurate upscaling information.

According to an embodiment, the processor 110 may obtain upscalinginformation of the input image by inputting the feature information ofthe input image to the first AI model.

For example, the processor 110 may obtain the probability informationfor each of the plurality of upscaling ratios by inputting the featureinformation of the input image into the first AI model, and may identifythe upscaling ratio of the input image based on the at least one of themaximum value or a value greater than or equal to a threshold valueamong obtained probability information. As another example, theprocessor 110 may obtain the probability information for each of theplurality of original resolution information by inputting the featureinformation of the input image into the first AI model, and may obtainthe original resolution information of the input image based on the atleast one of the maximum value or a value greater than or equal to athreshold value among obtained probability information.

In this case, the first AI model may be trained to obtain the upscalinginformation based on the feature information of the image. Here, thefeature information of the image may be information obtained in aspecific region of the image. For example, the feature information ofthe image may be information obtained in the edge region including theedge of the image, and this is because the edge region is a region inwhich a pixel value change is large by the upscaling of the image.However, the embodiment is not limited thereto, and the featureinformation may be obtained in at least one of a texture region and aflat region according to cases.

Here, the first AI model may be trained by using feature informationobtained in a plurality of training images and upscaling information(e.g., upscaling ratio or original resolution) of each of the pluralityof training images as input/output training data pairs. For example, asshown in FIG. 4A, (first feature information obtained in the firsttraining image, upscaling information of the first training image),(second feature information obtained in the second training image,upscaling information of the second training image), . . . , (Nthfeature information obtained from the Nth training image, the upscalinginformation of the Nth training image) can be used as the input/outputtraining data pair.

According to another embodiment, the processor 110 may obtain theupscaling information of the input image by inputting a whole or a partof the input image into the first AI model. In this example, the firstAI model can be a model that is trained to obtain upscaling informationbased on a whole or a part of an image. Here, a part of the image mayinclude at least a part of an image that includes an interest region, aportion of an image that includes a particular pixel feature (e.g., anedge).

The first AI model may be trained using the upscaling ration informationof the whole image (or a part of the image) and the corresponding imageas input/output training data pairs. For example, as illustrated in FIG.4B, (first training image, upscaling information of the first trainingimage), (second training image, upscaling information of the secondtraining image), . . . (nth training image, upscaling information of then^(th) training image) as the input/output training data pairs.

The learning of the AI model may mean that a predefined operation ruleor an AI model is created so as to perform a desired feature (orpurpose) by making a basic AI model (for example, an AI model includingarbitrarily random parameters) trained using a lot of training data by alearning algorithm. The training may be performed through a separateserver and/or system, but is not limited thereto, and may be performedin an electronic apparatus. An example of learning algorithm includessupervised learning, unsupervised learning, semi-supervised learning, orreinforcement learning, but is not limited thereto.

The first AI model may be implemented as, for example, convolutionalneural network (CNN), recurrent neural network (RNN), restrictedBoltzmann machine (RBM), deep belief network (DBN), bidirectionalrecurrent deep neural network (BRDNN), or deep Q-networks, but is notlimited thereto.

According to an embodiment, the processor 110 may obtain downscalingratio of the input image based on the obtained upscaling information.

For example, if the upscaling ratio is obtained, the processor 110 maydetermine (or identify) inverse of the obtained upscaling ratio as thedownscaling ratio. For example, if the upscaling ratio is 2, theprocessor 110 may determine 1/2, which is the inverse of the upscalingratio, as the downscaling ratio, and when the obtained upscaling ratiois 2.1, the processor 110 may determine 1/2.1, which is the inverse ofthe upscaling ratio, as the downscaling ratio. Alternatively, if theoriginal resolution information is obtained, the processor 110 maydetermine the ratio of the resolution of the input image to theresolution of the input image, that is, the ratio of the “resolution ofthe original resolution/resolution of the input image” as thedownscaling ratio.

Alternatively, after obtaining the upscaling ratio, the processor 110may adjust the upscaling ratio within the threshold range, and thendetermine the inverse of the adjusted upscaling ratio as the downscalingratio. For example, if the obtained upscaling ratio is 2.1, theprocessor 110 may adjust the upscaling ratio to 2 (e.g., an integermultiple) by applying an approximate scaling of 2/2.1, and thendetermine 1/2 which is the inverse of the adjusted upscaling ratio, asthe downscaling ratio. Similarly, if the original resolution informationis obtained, the ratio of the “resolution of the originalresolution/resolution of the input image” may be adjusted.

As another example, if the upscaling ratio is obtained, the processor110 may estimate the resolution of the original image based on theobtained upscaling ratio, and obtain the downscaling ratio of the inputimage based on the estimated resolution. Specifically, the processor 110may estimate the resolution of the original image based on the obtainedupscaling ratio, when the upscaling ratio is obtained based on theoutput of the first AI model. For example, the processor 110 mayestimate one of the predetermined plurality of resolutions as theresolution of the original image based on the resolution of the originalimage and the obtained upscaling ratio. For example, the predeterminedplurality of resolutions may include a variety of resolutions, forexample, 720×480, 1280×720, 1920×1080, 2560×1440, 3840×2160, 7680×4320,or the like.

For example, when the resolution of the input image is 4K UHD of3840×2160 and the identified upscaling ratio is 2, the processor 110 mayestimate that the resolution of the original image is FHD of 1920×1080.As another example, when the resolution of the input image is 4K UHD of3840×2160 and the identified upscaling ratio is 2.1, the processor 110may estimate that the resolution of the original image is FHD of1920×1080.

If the downscaling ratio is determined as described above, the processor110 may downscale (or down-sample) the input image based on thedownscaling ratio, and then scale the downscaled image based on theoutput resolution. For example, if the resolution of the input image is4K UHD of 3840×2160 and the output resolution is downscaled according tothe 1/2 downscaling ratio, if the output resolution is 8K UHD, theoutput image may be obtained by upscaling the downscaled image by twotimes. As for the downscaling method, various related-art methodsincluding a sub-sampling may be used. For example, the downscaling maybe performed by converting red-green-blue (RGB) data into YUV data(e.g., Y′UV, YUV, YCbCr, YPbPr), and reducing U, V components (colordifference information) contrasted with the Y component (brightnessinformation).

According to an embodiment, the processor 110 may upscale the downscaledimage using upscaling functional blocks for high resolution imageprocessing.

In one example, the processor 110 may upscale the downscaled image usinga second AI model that is trained to perform super resolution.Specifically, the processor 110 may obtain an upscaled image byinputting the downscaled image into a second AI model. In this case, thesecond AI model may be trained using a plurality of training images andupscaled images corresponding to each of the training images asinput/output training data pairs. Here, the plurality of training imagesmay be images of a variety of resolutions. For the second AI model, forexample, a CNN-based very deep super resolution (VDSR) technique (JiwonKim, et al., Accurate Image Super-Resolution Using Very DeepConvolutional Networks, CVPR 2016), Enhanced Deep Residual Networks forSingle Image Super-Resolution (EDSR), Deeply-Recursive ConvolutionalNetwork for Image Super-Resolution (DRCN).” Proceedings of the IEEEConference on Computer Vision and Pattern Recognition. 2016.),Multi-scale deep super-resolution system (MDSR) may be used, but theembodiment is not limited thereto.

According to one embodiment, the processor 110 may identify a presetregion in the input image, obtain the feature information for the pixelincluded in the identified region, and use it as the input data of thefirst AI model. Here, the preset region may include at least one of anedge region, a texture region, or a flat region.

According to one example, the processor 110 may identify a region ofinterest in the input image first, and then identify a predeterminedfeature region in the region of interest. For example, when there is animage within an image such as a news or a home shopping clip in a realimage, or an image that includes a subtitle or a face, there may be adifference in image quality and thus, a region of interest may beidentified first and then a predetermined feature area may be identifiedfrom the region of interest. Here, the region of interest may be aregion including a particular object (e.g., an object centering on aclear edge such as a building, a person, a text, etc.), a regionsatisfying a specific pixel value distribution (e.g., distribution inwhich pixel value differences are large among pixel regions), or thelike. However, the embodiment is not limited thereto, and apredetermined feature region may be identified without identifying theregion of interest in the input image.

In one embodiment, the processor 110 may obtain the feature informationfor the pixels included in the edge region. Here, the edge is a regionwhere the spatially adjacent pixel values are changing rapidly, and thedifference between adjacent pixel values may be greater than or equal toa threshold value. For example, the edge may be a region in which thebrightness of an image changes rapidly from a low value to a high valueor from a high value to a low value. For example, if a differencebetween the adjacent pixel values is greater than or equal to apredetermined reference value, the region may be determined as an edge.A boundary or a text of an object may be determined as an edge region.In one embodiment, the edge region may be a region including edge pixelscorresponding to edges and a pixel region around the edge pixels.

For example, the processor 110 may obtain the feature information basedon pixels outside a margin region with reference to a center pixelincluded in the identified edge region. As another example, theprocessor 110 may blur the identified edge region, set (or identify) amargin region with reference to the center pixel included in the blurrededge region, and obtain the feature information based on the pixelsother than the set margin region. Hereinbelow, the processor 110 blursthe identified edge region and the obtains the feature information.

FIG. 5 is a view to describe a method for obtaining feature informationinput to the first AI model according to an embodiment.

Referring to FIG. 5, the processor 110 detects an edge region in theinput image in operation S510. Here, the edge region may be included ina region of interest (interested object or text), but it is not limitedthereto.

In one example, the processor 110 may use an edge detection filter todetect edge regions. For example, the processor 110 may apply a primaryor a secondary edge detection filter to the input image to obtain afiltered signal that includes edge intensity and edge directioninformation (direction perpendicular to gradient), and thereby detectedge regions.

The processor 110 may also expand the edge region detected by blurring(or smoothing) the detected edge region in operation S520. For example,as illustrated in FIG. 6A, the Gaussian distribution may have a form inwhich zero on the X-axis has a larger weight, and the weight decreasestoward the +/− portion. When the Gaussian distribution is applied to themask 60 in 3*3 format, the center of the mask 60 may have a largeweight, and the weight decreases toward the edge of the mask 60.However, the values shown in FIG. 6A are examples, and the filteringvalues may vary in accordance with a sigma value of the Gaussianfunction. As illustrated in FIG. 6B, the processor 110 may apply theGaussian mask 60 to the detected edge region and blur the edge region.Generally, the Gaussian filter is used as a filter to remove the noisegenerated by the normal distribution through the smoothing and theprobability distribution. However, in one embodiment, the Gaussianfilter may function to smooth the pixel region identified as an edge inthe image and extend a target region to the surrounding pixel regionincluding the edge pixel. For example, as shown in FIG. 7A, the edgeregion may be extended to a dotted line through smoothing, and thereby,the target region may be extended. Thus, more accurate upscaling ratiomay be detected.

In operation S530 of FIG. 5, the processor 110 may set a margin regionin the blurred region and obtain the feature information by detectingpixels outside the margin region. Specifically, the processor 110 mayobtain information about the pixel that is present in the region outsidethe margin region (dotted line) that is set with reference to the edgepixel detected as shown in FIG. 7A. Here, the size of the margin regionmay be determined to a preset size, or may be set based on the size ofthe filter used for blurring, the coefficient value of the filter, orthe like. For example, the predetermined size may be set by consideringthe upscaling ratios that are most commonly performed in a generalimage, or may be set by considering the maximum possible upscaling ratioin the present image. The reason is that the pixel value (or similarpixel value) of the edge pixel may be extended to the edge peripheralregion according to the upscaling ratio of the image.

According to an example, the processor 110 may map first identificationinformation if the target pixel belongs to the margin region, and maymap second identification information if the target pixel does notbelong to the margin region and generate the feature information. Forexample, if binary identification information is used, the firstidentification information may be 0 and the second identificationinformation may be 1, but is not limited thereto. For convenience ofdescription, the first identification information is set to “0” and thesecond identification information is set to “1.”

According to an embodiment, the processor 110 applies the predeterminedsize of the pixels to the pixels included in the image, assignsidentification information to each pixel by identifying whether thepixels in the window belong to the margin region, and obtains thefeature information by arranging the identification information in thepredetermined order. Specifically, the processor 110 may identifywhether the pixels within the window belong to a margin region by movingthe position of the pre-determined size window into at least one pixelinterval. For example, the processor 110 may obtain the feature valuethat corresponds to each position by moving the position of the windowin one pixel interval, but the embodiment is not limited thereto, and itis possible to obtain a feature value that corresponds to each positionby moving the position in two or more pixel intervals.

According to an example, when a 5-pixel size window is applied to theblurred image as shown in FIG. 7B, 01001 may be obtained by sorting theidentification information 1 of the pixel 711 at the right edge relativeto the center pixel 710>the identification information 0 of the pixel712 at the left edge>identification information 0 of the pixel 713adjacent to the right side>identification information 1 of the pixel 714adjacent to the left side>identification information 0 of the centerpixel 710 in order at the positions of bit0 (20), bit1 (21), bit2 (22),bit3 (23), and bit4 (24). Thereafter, the processor 110 may obtain thefeature value “9” by converting the identification information into adecimal number (2{circumflex over ( )}4*0+2{circumflex over( )}3*1+2{circumflex over ( )}2* 0+2{circumflex over( )}1*0+2{circumflex over ( )}0*1=9).

Here, the processor 110 may obtain the histogram information based onthe frequency of each of the obtained feature values. For example, eachof the feature values represents bin, i.e., a section, and each featurevalue, i.e., the frequency of each bin, may be calculated. For example,the feature value “9” may be “bin9,” and each feature value, that is,frequency of each bin, that is, histogram information, may be obtainedas the feature information of the input image.

According to another embodiment, the processor 110 may obtain thefeature information by setting the size of window differently andapplying different strides for each size of window. Here, the stridemeans an interval among pixels that generate identification informationin the window.

For example, the processor 110 may set the different sizes of window todifferent types and obtain the feature information as illustrated inFIG. 7C.

As shown in a first line of FIG. 7C, the processor 110 may set thefeature value obtained by applying the 5-pixel size window to type 1,and as shown in a second line of FIG. 7C, the processor 110 may set thefeature value obtained by applying the 7-pixel size window to type 2,and as shown in a third line of FIG. 7D, set the feature value obtainedby applying the 9-pixel size window to type 3, and set the feature valueobtained by applying the window having another size to another type(e.g., type 4, type 5).

In order to obtain the feature value of type 1 illustrated in the firstline of FIG. 7C, the processor 110 may apply the window in the 5-pixeland stride of 0. A method of obtaining the feature value is the same asshown in FIG. 7B and thus will not be further described.

In order to obtain the feature value of type 2 shown in the second lineof FIG. 7C, the processor 110 may apply a 9-pixel window and stride 1.Specifically, the processor 110 may obtain the feature information ofthe 5-bit size by arranging the identification information of each ofthe four adjacent pixels as one-pixel interval on both sides of thecentral pixel basis in a predetermined order. For example, when a9-pixel size window is applied as shown in the second line of FIG. 7C,00110 may be obtained by sorting the identification information 0 of theright first pixel 721 with respect to the center pixel 710>theidentification information 1 of the left second pixel 722>identificationinformation 1 of the right third pixel 723>identification information 0of the fourth left pixel 724>identification information 0 of the centerpixel 720 at the positions of bit0 (2⁰), bit1 (2¹), bit2 (2²), bit3(2³), and bit4 (2⁴). That is, the processor 110 may obtain the featurevalue of type 2 by sorting identification information of pixels spacedapart at one-pixel interval in a predetermined order within a window ofa 9-pixel size. Then, the processor 110 may obtain the feature value “6”by converting to a decimal number (2{circumflex over( )}4*0+2{circumflex over ( )}3*0+2{circumflex over ( )}2*1+2{circumflexover ( )}1*1+2{circumflex over ( )}0*1=9).

By using the same method, the processor 110 may obtain the featurevalues of different types, for example, type 3 and type 4, bydifferently setting the window size and the stride size.

Here, the processor 110 may obtain histogram information based on thefrequency of each of the obtained type feature values. For example, theprocessor 110 may obtain feature information for each type, such astype1bin9, type2bin6, type3bin0, and type4bin8.

In the above-described embodiment, it has been described that thefeature information is obtained by aligning the identificationinformation in the left-right-left-right order with reference to thecenter pixel, but this is merely one embodiment. For example, it ispossible to obtain the feature information according to various rules byarranging the identification information of the left-most pixel at thepositions of bit0(2⁰), bit1(2¹), bit2(2²), bit3(2³), bit4(2⁴) in order,or pixel identification information, or arranging the identificationinformation of the left-most pixel in the bit0(2⁰), bit1(2¹), bit2(2²),bit3(2³), bit4(2⁴) positions in order.

In the embodiment above, although it has been described that the windowsize and the stride size are adjusted to obtain feature values of thesame size (for example, 5-digit binary number) for each type, but theembodiment is not necessarily limited thereto, and it is also possibleto vary the size of the feature values by types. For example, it ispossible to obtain the feature value of greater size (e.g., 6-digitbinary) by maintaining the size of stride even though the size of thewindow is increased.

In the above-described embodiment, it is assumed that the processor 110obtains the feature information after blurring the identified edgeregions, but it is possible that the process other than the blurring maybe applied in the same manner when the feature information is obtainedwithout blurring the identified edge region.

Returning to FIG. 2, according to another embodiment, the processor 110may perform a first image processing prior to upscaling the downscaledimage. In addition, the processor 110 can obtain an output image byperforming a second image processing on the upscaled image. For example,the first image processing may include at least one of a noisereduction, a noise reduction simple, detail enhancement or detailenhancement simple, and the second image processing may include at leastone of tone mapping, contrast enhancement, color enhancement, or framerate conversion, but is not limited thereto. For example, the processor110 may perform pre-filtering on the input image for noise reductionprocessing. In general, noise is generated in a process of compressingor transmitting an image or the like. The processor 110 may performnoise reduction using non-local filtering and self-similarity, asmoothing filter by low pass filtering, or the like.

When the first image quality processing is performed after downscalingthe image, there is an effect of using hardware with low complexity inthe first image quality processing and saving hardware implementationcosts.

If the input image is an original image rather than an upscaled imageand a separate upscaling process is not required, the processor 110 mayperform image processing for the input image and then perform a secondimage quality processing to obtain an output image. Here, the imagequality processing performed on the input image may include at least oneof a noise reduction simple or detail enhancement simple. That is, theimage quality processing performed on the original image may be somewhatdifferent from the first image quality processing method (at least oneof noise reduction or detail enhancement) performed on the upscaledimage, but the same image quality processing method may be used.However, for the second image quality processing, the same image qualityprocessing method may be used regardless of the resolution of the inputimage and whether or not the image upscaling is processed.

According to an embodiment, the processor 110 may identify a timing whena scene is changed in the input image and may perform downscaling basedon a timing when the scene is changed.

According to one example, when the upscaling information meaningful in aparticular scene section of the input image is obtained, the processor110 may perform downscaling from the next scene based on thecorresponding upscaling information. For example, the processor 110 maydownscale from a frame included in the second scene section after thefirst scene section based on the upscaling ratio obtained in the firstscene section and upscale the downscaled frame to obtain an outputframe. The processor 110 may not perform the downscaling from the nextframe when the upscaling information is obtained, but performdownscaling on the basis of the scene change timing, thereby preventingthe resolution change due to the frequent change in the upscaling ratio.

According to an example, the processor 110 may identify when the scenechanges based on scene change information embedded in the image, forexample, a scene change flag. For example, the scene change flag may beincluded in one region of metadata, and may have a value of “0” if thescene is maintained, but may have a value of “1” when the scene ischanged. However, the embodiment is not limited thereto, and variousmethods can be used to identify the conventional algorithm. The scenecan be a scene according to a change in the space on the scenariodivided by a content producer. However, in some cases, the scene maymean a section having similar image quality feature, in which case, thescene may be divided into different scenes even in the same space on thescenario divided by the content producer depending on the brightness,the color, and the like, of the image.

The processor 110 may obtain the upscaling information (e.g., theupscaling ratio information, the original resolution information) inunits of a predetermined frame interval of the input image, and performdownscaling from the frame where the second scene starts after the scenesection of the threshold number when the same upscaling information ismaintained in a threshold number of scene sections after the first scenesection. This is because of the reason that the scene change informationor upscaling information may not be perfectly accurate and thus is toprevent the resolution from being changed for unnecessarily frequentlydue to frequent upscaling ratio changes. Here, the frame interval may bea time interval including only one frame, and may be a frame unit of oneframe. That is, the processor 110 may identify upscaling information ona frame-by-frame basis, and perform downscaling from a frame in which asecond scene after a threshold number of scene sections starts when theupscaling information identified for each frame is kept constant in athreshold number of scene sections after the first scene section.

FIG. 8 is a view to describe a method of scene-based downscalingaccording to an embodiment.

According to one example, it is assumed that an input image includingten scenes is received by the electronic apparatus 100 as shown in FIG.8. As shown in the first line of FIG. 8, it is assumed that scene 1 toscene 3 are 4K image, scene 4 to scene 8 are upscaled 4K image (UP4K),scene 9 is 4K image, scene 9 is 4K image, and scene 10 is upscaled 4Kimage (UP4K).

In this case, the processor 110 can obtain the upscaling information foreach frame as shown in the third line of FIG. 8, while detecting scenechange flag information in the input image as shown in the second lineof FIG. 8. For example, the processor 110 may obtain the upscalinginformation for each frame based on the output of the first AI model asdescribed above. In a timing when a scene is changed generally, thescene change flag “1” may be detected so that the processor 110 maydetermine the scene change timing, but in actual, the scene change flaginformation may be erroneously detected. Thus, in the embodiment, it isassumed that the scene change flag “1” is erroneously detected in anintermediate frame including scene 4, and scene change flag “0”, not“1”, is detected in timing when the scene is changed to scene 6 andscene 7. Further, it is assumed that the processor 110 may erroneouslydetermine the upscaling information for each frame, and thus it isassumed that the upscaling information for each frame is erroneouslydetected in some frames.

In this case, when the scene change flag “1” is detected, if the sameupscaling information is maintained more than a threshold number (e.g.,three frames), the processor 110 may perform the upscaling. For example,in the embodiment shown in FIG. 8, an upscaled 4K image (UP4K) upscaledfrom scene 4 may be input, but in practice, downscaling may be performedfrom scene 6 after the same upscaling information is maintained above athreshold number. In addition, the image of scene 9 is a 4K image, butmay be ignored, and processing for the upscaled 4K image (UP4K), thatis, the downscaling process can be maintained in scene 9.

As described above, when the processor 110 changes the downscaling onlyat the scene change timing, and if the same upscaling information iscontinuously detected by more than a threshold number (e.g., threeframes), the processor 110 can perform downscaling from the frame at thenext scene change timing. This is to apply the embodiment in aconservative manner as much as possible because the frequent resolutionchange can be excessively recognized by a viewer, and the scene changeinformation or the upscaling information is not perfectly accurate,either. That is, in one scene, every frame-different upscaling ratio anddifferent probability values can be estimated, but these may not beapplied as they are. This is because upscaling information can bechanged very frequently according to actual news, home shopping,advertisement, or channel change, and if the upscaling information isapplied as it is, a side effect may occur due to frequent resolutionchanges.

FIGS. 9A and 9B are views to describe a structure of the first AI modelaccording to an embodiment.

Referring to FIG. 9A, the first AI model may be implemented as a neuralnetwork including a plurality of neural network layers. For example, thefirst AI model may be implemented as a classification network.

According to an example, the input of the neural network may be thefeature information of an input image obtained according to the methodshown in FIGS. 7A to 7C. According to another example, the input of theneural network may be an input image itself or a portion of an inputimage. Hereinafter, the case where the input of the neural network isthe feature information of the input image according to an example willbe described.

According to an example, a plurality of neural network layers, asillustrated in FIG. 9B, for example, four dense layers may beimplemented to be sequentially connected. The number of the dense layeris not limited thereto.

Here, as shown in FIG. 9B, the dense layer refers to a layer of a typethat is combined with all neurons of a previous layer, and is alsoreferred to as a fully-connected layer. According to one example, theinput data may pass through a respective dense layer 910 and forexample, the number of output channels decreases as 128 channels→64channels→16 channels→five channels, and the five channels that are thelast output channel number may represent each upscaling ratio.

For example, if the feature information of the input image is obtainedas a feature value for each type, as shown in FIG. 7C, the feature valuefor each type may be the input data of the dense layer. A feature valuefor each type is input into the first dense layer by 128 channels, andfive classes, that is, five upscaling ratio values (e.g., ×1, ×2, ×3,×4, ×4 or more) and probability values for five classes, that is,probability values corresponding to five upscaling ratio values, can beoutput in the last dense layer.

According to an example, the feature values for each type may berandomly distributed over 128 channels and input. According to anotherexample, it is possible that the feature values for each type aregrouped into different channels based on each type and input. Forexample, the first type feature value may be input into 1^(st) to25^(th) channel, the second type feature value into 26^(th) to 50^(th)channel, the third type feature value input 51^(st) to 75^(th) channel,the fourth type feature value input 76^(th) to 100^(th) channel, and thefifth type feature value into the 100^(th) to 128^(th) channels. In thiscase, a parameter corresponding to each channel can be trained tocorrespond to a corresponding type. For example, the filter coefficientscorresponding to the first to 25^(th) channels in the first dense layermay be trained to reflect the feature of the first type feature value.

The output portion of the neural network may be implemented to enable anargmax processing 921 and softmax processing 922 as shown in FIG. 9B.Here, softmax is a function that normalizes the input value to the valuebetween 0 and 1 and always makes the sum of the output values to 1, andmay have a function to output the probability corresponding to eachclass, that is, the probability value by upscaling ratios. The argmaxfunctions as a function of selecting the highest probability among aplurality of labels, and in this case, it is possible to select a ratiohaving the greatest value among the probability values for eachupscaling ratio. That is, the original image may finally output theupscaled ratio.

According to one embodiment, the first AI model may be trained based ontraining data associated with various training images. For example, thefirst AI model may be trained based on information associated with aplurality of training images of which at least one of a resolution, acompression scheme, a compression rate, a data transfer rate, anupscaling ratio, an enhancement processing, or an order betweencompression and upscaling is different. For example, training images ofvarious types obtained based on various bit rates such as SD, HD, FullHD, or UHD, various bitrates such as 10 Mbps, 15 Mbps, or 20 Mbps,various bitrate types (e.g., variable bitrate type, constant bitratetype or average bitrate type, etc.), and various compression methodsMoving Picture Experts Group (MPEG) (e.g., MP2, MP4, MP7, etc.), JointPhotographic Coding Experts Group (JPEG), Advanced Video Coding (AVC),H.264, H.265, High Efficiency Video Codec (HEVC), VC-1, VP8, VP9, andAOMedia Video 1 (AV1) may be used for learning.

FIGS. 10A to 10F are views to describe a method for obtaining trainingdata for training the first AI model according to an embodiment.

According to one embodiment, the learning database (DB) for training ofthe first AI model may include training data generated according tovarious methods. For example, an external server performing the trainingof the first AI model may generate training data in a variety of ways byconsidering the real broadcast environment scenario. However, in somecases, the training of the first AI model may be performed by theelectronic apparatus 100, and the training data can be generated in theelectronic apparatus 100.

For example, learning DB including a plurality of training images ofwhich at least one of a resolution, compression method, compressionrate, data transmission rate, upscaling ratio, enhancement processing,compression and upscaling order, or image type is different may beprovided.

For example, the training image may be obtained by compressing theoriginal image in accordance with AVC method as illustrated in FIG. 10A,and then upscaling the compressed image.

As another example, the training image may be obtained by compressingthe original image in accordance with the HEVC method and then upscalingthe compressed image, as illustrated in FIG. 10B.

As a still another example, the training image may be obtained afterprocessing the original image through the enhance treatment, compressingthe enhanced image in accordance with the AVC method, and then upscalingthe compressed image, as illustrated in FIG. 10C.

As a still another example, the training image may be obtained byprocessing the original image by the enhancement treatment, compressingthe enhanced image in accordance with the HEVC method, and thenupscaling the compressed image, as illustrated in FIG. 10D.

As a still another example, the training image may be obtained bycompressing the original image in accordance with the AVC method,upscaling the compressed image, and then performing enhancementtreatment for the upscaled image as illustrated in FIG. 10E.

As a still another example, the training image may be obtained bycompressing the original image in accordance with the HEVC method,upscaling the compressed image, and then performing the enhancementtreatment for the upscaled image, as illustrated in FIG. 10F.

The first AI model according to an embodiment may be trained based ontraining data (for example, feature information and upscaling ratio)obtained from training images in various types as described above.

The embodiments shown in FIGS. 10A-10 F are specific examples to aid inunderstanding, and various compression schemes may be applied, includingat least one of MPEG (e.g., MP2, MP4, MP7, etc.), JPEG, AVC, H.264,H.265, or HEVC. In addition, the enhancement processing may include anoise reduction, a noise reduction simple, a detail enhancement, adetail enhancement simple, a tone mapping, a contrast enhancement, acolor enhancement, or a frame rate conversion. In addition, the imagetype was further derived based on two large-category environmentalcriteria of broadcast/STB, such as compression method, compressionratio, enhancement, upscaling, and compression order. In addition, theoriginal image may use various resolution images including at least oneof SD, HD, Full HD, or Ultra HD. Alternatively, the original image maybe a variety of types including at least one of news, drama, ordocumentary among movies. This is because the image feature may bedifferent depending on the image content types. It is also possible togenerate a training image by applying a variety of image processingwhich may influence the image feature such as image restoration, imagetransformation, or the like.

As described above, by training the first AI model using varioustraining images that can be used in the real broadcasting environment,an upscaling ratio which is as accurate as possible may be obtained inany input image.

FIGS. 11A to 11C are views to describe a method for upscaling processingusing the second AI model according to an embodiment.

According to an embodiment, the second AI model, that is, an AI modelfor upscaling of the downscaled image may be implemented as a learningnetwork model for super resolution process. The super resolutionindicates a processing of converting a low-resolution image to ahigh-resolution image through a series of media processing.

According to an embodiment, as shown in FIG. 11A, the processor 110 mayupscale the downscaled images using the second AI model 30 formed of aplurality of neural network layers. Each of the plurality of neuralnetworks includes a plurality of parameters (or a plurality of weightvalues), and may perform a neural network operation by performing analgorithm among a plurality of parameters and a previous calculationresult. The parameters included in a plurality of neural network layersmay be optimized by the training result of the AI model. For example,parameters may be renewed so that a loss value or a cost value obtainedfrom the AI model during the training process may be minimized. Theartificial neural network may include deep neural network (DNN) and mayinclude, for example, convolutional neural network (CNN), recurrentneural network (RNN), generative adversarial networks (GAN), restrictedBoltzmann machine (RBM), deep belief network (DBN), bidirectionalrecurrent deep neural network (BRDNN), or deep Q-networks, but is notlimited thereto.

As shown in FIG. 11B, the processor 110 may obtain a residual image 12by performing the interpolation processing 20 on the downscaled image 10and inputting the interpolated image 11 to the second AI model 30. Thatis, the second AI model 30 may be implemented as a residual neuralnetwork. Here, each of a plurality of layers forming the residual neuralnetwork may generate a residual image for the image interpolated byusing a filter including different parameters. Here, the parameter maybe the same as the weight (or coefficient) of the filter. In this case,the second AI model 30 may perform algorithm using various activationfunctions such as Identity Function, Logistic Sigmoid Function,Hyperbolic Tangent(tanh) Function, ReLU Function, Leaky ReLU Function,and the like. However, the second AI model 30 does not necessarilygenerate the residual image, and may process an image input by variousmethods according to an embodiment of the second AI model 30, and outputthe processed image.

In this case, the processor 110 can obtain the output image 13, that is,for example, a high-resolution image, by combining the interpolatedimage 11 with the residual image 12. Here, the interpolation processingmeans a process of scaling a low-resolution image to a high-resolutionimage. At least one interpolation method among, for example, bilinearinterpolation, nearest neighbor interpolation, bicubic interpolation,deconvolution interpolation, subpixel convolution interpolation,polyphase interpolation, trilinear interpolation, linear interpolation,and the like, may be used. The residual image can be an image includingonly residual image information. Here, the residual information may beinformation in accordance with a difference between an input image and areference image, and may include, for example, information such as edgedirection, edge strength, noise information or texture information, butis not limited thereto. According to another example, the residualinformation may include at least one of the grayscale information,brightness information, or the gamma information.

According to another example, as shown in FIG. 11C, the processor 110may obtain a residual image 12′ by inputting the downscaled image 10 tothe second AI model 30, and interpolated residual image 12″ byinterpolating 40 the residual image 12′. In addition, the processor 110may perform the interpolation process 20 for the downscaled image 10 toobtain the interpolated image 11. The processor 110 may obtain theoutput image 13, for example, a high-resolution image, by combining theinterpolated image 11 with the interpolated residual image 12″. That is,according to the embodiment shown in FIG. 11C, the residual image 12′may be obtained by inputting the downscaled image 10 to the second AImodel 30, unlike the embodiment of FIG. 11B.

According to another embodiment, an AI model other than the second AImodel 30, for example, a third AI model, may be further included. Inthis case, the second AI model and the third AI model may be operatedsequentially or in parallel. For example, the processor 110 may inputthe downscaled image 10 to the second AI model, input the output of thesecond AI model to the third AI model, and then obtain an output imagebased on an image output from the third AI model. As another example,the processor 110 may input the downscaled image 10 to each of thesecond and third AI models and obtain an output image based on aplurality of images output in parallel from the second and third AImodels. For example, the second AI model is a model that generates afirst residual image, and the third AI model may include a model thatgenerates a second residual image. Alternatively, the second AI model isa model for the upscaling of the resolution, and the third AI model canbe a model for one of the above-mentioned diverse image processing(e.g., noise reduction). The second AI model may be a model for theobject region processing, and the third AI model may be a model for thebackground region processing.

FIG. 12 is a view to describe an operation of an electronic apparatusaccording to an embodiment.

FIG. 12 illustrates that the electronic apparatus 100 is implemented asthe 4K UHD display device. In FIG. 12, for convenient description, it isillustrated that the processor 110 includes an image analysis module1210, a downscaler 1220, and an upscaler 1240. The image analysis module1210, the downscaler 1220, and the upscaler 1240 may be implemented asat least one software, at least one hardware, or combination thereof inthe processor 110.

In FIG. 12, for convenient description, it is assumed that resolution ofan image input to the electronic apparatus 100 is any one of, forexample, SD, HD, FHD, or UHD.

The resolution information may be included in the image input to theelectronic apparatus 100, and when the resolution of the input image isSD, the processor 110 determines that upscaling is required, and mayprovide the received image to the upscaler 1230. However, according toan embodiment, the processor 110 needs to determine whether the inputimage is an original image or an upscaled image, when the resolution ofthe input image is one of the HD, FHD, or UHD. In this case, theprocessor 110 may provide the input image to the image analysis module1210.

The image analysis module 1210 may analyze the input image to determinewhether the input image is the original image or the upscaled image. Forexample, the first AI model described above can be used to obtainupscaling information of the input image and determine whether the inputimage is the original image or the upscaled image based on the obtainedupscaling information.

The image analysis module 1210 may provide the upscaler 1230 with aninput image when the input image is an original image of HD or FHD. Thisis because the output resolution is UHD and upscaling of the resolutionis necessary. The image analysis module 1210 may determine that theinput image is not necessary for upscaling if the input image is anoriginal image of UHD, and may not provide the upscaler 1230 with thereceived image. If the input image is one of the upscaled image of HD,FHD, or UHD, the image analysis module 1210 may determine that it isnecessary to perform downscaling and upscaling according to anembodiment, and may provide the downscaler 1220 with the input imagealong with the corresponding downscaling information (e.g., adownscaling ratio). For example, the image analysis module 1210 mayobtain downscaling information based on the obtained upscalinginformation.

The downscaler 1220 may downscale the input image based on thedownscaling information (e.g., the downscaling ratio) provided from theimage analysis module 1210. However, it is also possible that the imageanalysis module 121 provides the upscaling information to the downscaler1220 and the downscaler 1220 obtains the downscaling information basedon the upscaling information.

The downscaler 1220 may perform downscaling for the input image andprovide the upscaler 1230 with the downscaled image. For example, thedownscaler 1220 may downscale the input image to a resolution of theoriginal image.

The upscaler 1230 may obtain an output image by upscaling the downscaledimage received from the downscaler 1220, that is, the downscaled imagewith the resolution of the original image, based on the outputresolution. For example, the upscaler 1230 may obtain a high-resolutionoutput image by upscaling the downscaled image using the upscalingfunction block to process a high-resolution image. For example, theprocessor 110 may use a dictionary learning and sparse representation(DLSR) technique to upscale 1340 images for which image qualityprocessing is performed. The DLSR is the technology to enhance theresolution of the input image using the CNN model that is trained basedon the training DB including the high-resolution original image and thelow-resolution image. According to an embodiment, the DLSR technologymay maximize a texture part generation effect in an image through a DLSRprocessing by including a generative adversarial insurance networks(GAN). In general, the GAN may produce data that is not present in thegenerator, and the discriminator may operate to produce data that issimilar to real data through training to distinguish authentic data andfake data by competitively training two data. By applying the GAN to theimage quality enhancement, it is possible to naturally generate textureswhich are not present in the original image but are similar to theconventional image feature and thus, it is expected to enhance detailsin the texture region. Accordingly, it is possible to obtain ahigh-resolution output image. However, DLSR can be designed to begenerally upscaled to integer ratios of ×2, ×3, and ×4. As a result, forthe resolution of 720×480 to be upscaled to 3840×2160, upscaling of thedecimal point is required. For this purpose, various conventionalupscaling schemes can be used for upscaling of non-integer ratios.

FIG. 13 is a view to describe a modified embodiment of an operation ofthe electronic apparatus illustrated in FIG. 12. The configurations ofFIG. 13 overlapped with the configurations of FIG. 12 will not befurther described.

The processor 110 determines the upscaling ratio of the input image bythe image analysis 1310 of the input image. As shown in FIG. 12, theprocessor 110 performs the image analysis 1310 for an image for whichupscaling of HD, FHD, or UHD may be performed, and may not perform aseparate image analysis for the SD image, and does not perform aseparate image analysis for the SD color image. For example, theprocessor 110 may identify upscaling status and upscaling information ofthe input image use the first AI model described above.

If it is determined that the input image is an image obtained byupscaling the original image, the processor 110 can downscale the imagebased on the downscaling ratio of the input image signal 1320. Forexample, the processor 110 may downscale the upscaled image to theestimated resolution of the original image. For example, if it isidentified that the 4K UHD input image is obtained by upscaling theoriginal image of SD resolution to 4K UHD, the processor 110 candownscale the input image to the SD resolution.

The processor 110 may then perform first image processing 1340 for theimage downscaled to the estimated original resolution. The first imagequality processing may be an original resolution (e.g., SD resolution)based image quality processing (e.g., noise reduction, detailenhancement). In this case, image quality processing is performed for adownscaled image, for example, a low-resolution image and thus, it ispossible to calculate the statistical values of the average/histogramvalues for image quality processing by hardware and/or software with lowcomplexity.

The processor 110 may perform upscaling 1330 for the image proceed withthe first image quality processing to the output resolution (forexample, 4K UHD).

The processor 110 may perform second image quality processing 1350 forthe image upscaled to the output resolution. The second image qualityprocessing may be image quality processing (for example, tone mapping,contrast enhancement, and color enhancement) based on the outputresolution.

The processor 110 may obtain an output image by performing 1360 thirdimage quality processing (for example, frame rate conversion) for theimage processed with second quality processing.

As a result of the image analysis 1310, if the input image is 4K UHDoriginal which has not been upscaled, the processor 110 may performimage quality processing 1335 (for example, noise reduction simple,detail enhancement simple) based on the original resolution for thecorresponding image and then obtain the output image by performing onlythe second image quality processing and third image quality processingbased on the output resolution. This is because it is not necessary toperform the upscaling process in case of the 4K UHD original image ofwhich resolution is the same as the output resolution. The processor 110may perform the first image processing 1340, the upscaling 1330, thesecond image processing 1350, and the third image processing 1360 whenthe input image is an SD image.

The above image processing based on the input resolution and imageprocessing based on the output resolution is merely an example, and someof the image processing may be omitted, or additional image processingother than the corresponding image processing may be performed.

FIG. 14 is a view to describe an image analysis operation illustrated inFIGS. 12 and 13 in a greater detail.

Referring to FIG. 14, the processor 110 may identify 1211 a region ofinterest in the input image first and then extract 1212 a feature valuein the identified region of interest. Identifying the region of interestmay be omitted depending on cases.

Subsequently, the processor 110 may input the extracted feature valueinto a classification network 1213 to obtain upscaling information, forexample, an upscaling ratio. Here, the classification network may beimplemented with the first AI model described above. For example, theprocessor 110 may obtain an upscaling rate corresponding to each frameunit.

The processor 110 may detect 1214 the scene change timing in the inputimage and perform downscaling by determining 1215 the downscaling ratiobased on the obtained upscaling ratio and the detected scene changetiming. For example, the processor 110 may determine the downscalingratio for a frame corresponding to the scene change timing, that is, aframe in which a next scene begins and perform downscaling from thecorresponding frame. When the same upscaling ratio is detected for morethan or equal to a threshold number (for example, three frames) in aconsecutive manner, the processor 110 may perform downscaling from theframe at the scene change timing thereafter.

During reproduction of one scene, if upscaling ratio values aredifferent and downscaling is performed whenever the upscaling ratiobecomes different for each frame, it is not possible to avoid visibilityof a user for image quality change. In order to avoid this problem, ascene detection may be performed and down-sampling may be initiated onlyat the timing of scene change.

FIG. 15 is a view to describe a hardware structure of an electronicapparatus according to an embodiment.

FIG. 15 illustrates a chip structure of the processor 110 according toan embodiment, and the processor 110 chip may include solutions forvarious processing. Here, the solution refers to a reusable functionalblock and may be a hardware or software functional block. Hereinafter, acase where a solution is implemented as custom hardware will bedescribed for convenient description.

For example, the processor 110 chip for image processing may beimplemented so that the custom hardware of the memory 120, CPU 111, avideo decoder 112, DS 113, NR 114, DE 115, UP 116, FRC 117 are connectedby a bus. In FIG. 15, the configurations corresponding to the secondimage quality processing part among the configurations of FIG. 13 areomitted for convenience of description.

Here, the video decoder 112 is the custom hardware for the decodingprocess. According to an embodiment, the input image may be compressedimage data, and the video decoder 112 may perform a decoding process forthe compressed image. For example, the input image may be image datathat has been encoded by a frequency conversion-based image compressionmethod. The video decoder 112 may decode the compressed image datathrough at least one of a process of generating the quantized residualdata through entropy decoding of the compressed image data,inverse-quantizing of the quantized residual data, converting theresidual data of the frequency domain component into a spatial domaincomponent, generating the prediction data, or reconstructing the imageby using the prediction data and the residual data. The decoding methodmay be implemented by an image reconstruction method corresponding toone of the image compression method using frequency conversion used inthe encoding performed in the image compression, such as the MPEG-2, theH.264, the MPEG-4, the HEVC, the VC-1, the VP8, the VP9, the AV1, or thelike.

The DS 113 is the custom hardware for the downscaling processing, the NR114 is the custom hardware for the noise reduction processing, the DE115 is the custom hardware for the detail enhancement processing, wherethe UP 6 is the custom hardware for the upscaling, e.g., superresolution processing, and the FRC 117 can be a custom hardware forframe rate conversion. For example, the CPU 111 may control theoperation of the DS 113 based on information about the first AI modelstored in the memory 120. Further, the CPU 111 may control the operationof the UP 116 based on information about the second AI model stored inthe memory 120. However, the embodiment is not limited thereto, and theoperation of the DS 113 and the UP 116 may be controlled by otherprocessors such as NPU.

However, according to another embodiment, the various custom hardwareincluded in the processor 110 chip may be implemented with at least onesoftware or a combination of at least one software and at least onehardware. For example, logic corresponding to some functions of thevideo decoder 122 may be implemented within the video decoder 112, andlogic corresponding to other functions of the video decoder 112 may beimplemented as software executable by the CPU.

FIG. 16 is a view illustrating an embodiment of an electronic apparatusaccording to an embodiment.

Referring to FIG. 16, the electronic apparatus 100″ may include theprocessor110, a memory 120, an inputter 130, a display 140, an outputter150, and a user interface 160. The configuration of FIG. 10 which areoverlapped with the configurations of FIG. 2 will not be furtherdescribed.

The inputter 130 may receive various types of content. For example, theinputter 130 may receive an audio signal by streaming or downloadingfrom an external device (for example, a source device), an externalstorage medium (for example, a universal serial bus (USB) device), anexternal server (for example, a web server, etc.) through communicationmethods such as an access point (AP)-based Wi-Fi (wireless LAN network),Bluetooth, Zigbee, wired/wireless local area network (LAN), wide areanetwork (WAN), Ethernet, IEEE 1394, high definition multimedia interface(HDMI), universal serial bus (USB), mobile high-definition link (MHL),advanced encryption standard (AES)/European broadcasting union (EBU),optical, coaxial, or the like. Here, the image signal may be a digitalimage signal of any one of the SD, HD, full HD, or ultra HD images, butthis is not limited thereto.

The display 140 may be implemented as a display including aself-emitting element or a display including a non-self-limiting elementand a backlight. For example, the display 140 may be implemented as adisplay of various types such as a liquid crystal display (LCD), organiclight emitting diodes (OLED) display, light emitting diodes (LED), microLED, mini LED, plasma display panel (PDP), quantum dot (QD) display,quantum dot light-emitting diodes (QLED), or the like. In the display140, a backlight unit, a driving circuit which may be implemented as ana-si TFT, low temperature poly silicon (LTPS) TFT, organic TFT (OTFT),or the like, may be included as well. In the meantime, the display 140may be implemented as a touch screen coupled to a touch sensor, aflexible display, a rollable display, a third-dimensional (3D) display,a display in which a plurality of display modules are physicallyconnected, or the like. The processor 110 may control the display 140 tooutput an output image that is obtained according to variousembodiments. The output image may be a high-resolution such as 4K, 8K,or higher.

The outputter 150 outputs a sound signal. For example, the outputter 150may convert the digital sound signal processed by the processor 110 intoan analog sound signal, amplify and output the analog sound signal. Forexample, the outputter 150 may include at least one speaker unit, a D/Aconverter, an audio amplifier, or the like, capable of outputting atleast one channel. According to an example, the outputter 150 may beimplemented to output various multi-channel sound signals. In this case,the processor 110 may control the outputter 150 to process the inputsound signal in accordance with the enhanced processing of the inputimage. For example, the processor 110 may convert an input two-channelsound signal into a virtual multi-channel (for example, 5.1 channel)sound signal, recognize a position where the electronic apparatus 100″is located to process the signal as a cubic sound signal optimized to aspace, or provide an optimized sound signal according to the type ofinput image (for example, a content genre).

The user interface 160 may be implemented as a device such as a button,a touch pad, a mouse, a keyboard, a remote control receiver ortransmitter or a touch screen capable of performing the above-describeddisplay function and input function. The remote control transceiver mayreceive a remote control signal from an external remote controllerthrough at least one communication methods such as an infrared rayscommunication, Bluetooth communication, or Wi-Fi communication, ortransmit the remote control signal.

The electronic apparatus 100″ may further include a tuner and ademodulator according to an embodiment. A tuner (not shown) may receivea radio frequency (RF) broadcast signal by tuning a channel selected bya user or all pre-stored channels among RF broadcast signals receivedthrough an antenna. The demodulator (not shown) may receive anddemodulate the digital intermediate frequency (IF) signal and digital IF(DIF) signal converted by the tuner, and perform channel decoding, orthe like. The input image received via the tuner according to oneembodiment may be processed via the demodulator (not shown) and thenprovided to the processor 110 for image processing according to oneembodiment.

FIG. 17 is a flowchart to describe a method for controlling anelectronic apparatus according to an embodiment.

According to a method for controlling the electronic apparatus of FIG.17, upscaling information of the input image is obtained using the firstAI model in operation S1710. The first AI model may be trained to obtainupscaling information of an image. The upscaling information of theinput image may include at least one of the upscaling ratio of the inputimage or original resolution information of the input image.

The input image is downscaled based on the obtained upscalinginformation in operation S1620.

The output image is obtained by upscaling the downscaled image based onthe output resolution in operation S1630.

In operation S1630 of obtaining the output image, first image qualityprocessing for the downscaled image may be performed, the imageprocessed with the first image quality processing may be upscaled, andthe output image may be obtained by performing the second image qualityprocessing for the upscaled image.

In operation S1630 of obtaining an output image, a downscaled image maybe upscaled using the second AI model. The second AI model may betrained to perform super resolution processing.

According to an embodiment, the first AI model may be trained to obtainthe upscaling information of the image based on the feature informationof the image. In this case, in operation S1610 of obtaining theupscaling information of the input image, upscaling information of theinput image may be obtained by inputting the feature informationobtained from the input image into the second AI model.

In operation S1610 of obtaining upscaling information of the input imagemay include identifying an edge region in the input image and obtainingfeature information for a pixel included in the identified edge region.

Also, in operation S1610 of obtaining the upscaling information of theinput image, the identified edge region may be blurred, the featureinformation is obtained for the pixels outside the margin region withrespect to the center pixel included in the blurred edge region, and theupscaling information of the input image may be obtained by inputtingthe obtained feature information into the first AI model.

In operation S1610 of obtaining the upscaling information of the inputimage may include identifying a region of interest in the input imageand identifying an edge region included in the identified region ofinterest.

According to another embodiment, the first AI model can be a modeltrained to obtain upscaling information based on the image or edgeregions of the image. In this case, in step S1610 of obtaining theupscaling information of the input image, the input image or the imageincluding the edge region of the input image is input to the first AImodel to obtain an upscaling ratio of the input image.

In operation S1620 of downscaling the input image may includedetermining whether to downscale the input image based on a timing whena scene of the input image is changed.

Also, in operation S1620 of downscaling the input image, if theupscaling information is obtained in the first scene section of theinput image, downscaling may be performed from the frame included in thesecond scene section after the first scene section.

In operation S1620 of downscaling the input image, the upscalinginformation may be obtained in a predetermined frame section of theinput image and when the same upscaling information is used in thethreshold number of scene sections after the first scene section,downscaling may be performed from the frame in which the second scenesection begins after the scene sections in the threshold number.

According to an embodiment, the first AI model may be trained based oninformation related to a plurality of training images of which at leastone of a compression method, a compression rate, an upscaling ratio,enhancement processing status, order between compression and upscaling,or an image type is different.

According to various embodiments described above, after estimating theoriginal resolution of the image upscaled at the outside and thenupscaling the image through the high-performance upscaling technology, ahigh-resolution image may be provided.

By performing image quality processing which is hard to be processed foran upscaled image due to a reason of a hardware fee to a downscaledimage, there is an effect of using hardware with low complexity andreducing hardware implementation fee accordingly.

If the embodiment as described above is applied, even if the resolutionof the input image is the same as the resolution of the output image,there is a clear distinction in image quality. In this case, it can bedetermined that the embodiment of the disclosure is applied.

The various embodiments may be applied to not only display apparatusesbut also all the electronic apparatuses such as an image receivingdevice such as a set-top box and an image processing device, and thelike. Various embodiments described above may be performed through anembedded server provided in the electronic apparatus, or an externalserver of the image processing device.

The method according to various embodiments may be implemented as anapplication or software which may be installed in the conventionalelectronic apparatus.

In addition, the methods according to various embodiments may beimplemented only with software upgrade or hardware upgrade for theconventional electronic apparatus.

While the embodiments described above are capable of being prepared in aprogram or instruction that may be executed on a computer, the preparedprogram or instructions may be stored in a medium.

The medium may store computer-executable programs or instructions, storeor temporarily store programs or instructions for execution or download.In addition, the medium may be any of a variety of recording means orstorage means in which a single or several hardware is coupled, and maybe distributed over a network, without being limited to any medium thatis directly connected to any computer system. Examples of the medium mayinclude a magnetic medium such as a hard disk, a floppy disk, and amagnetic tape; an optical medium such as a compact disk read only memory(CD-ROM) or a digital versatile disk (DVD); a magneto-optical mediumsuch as a floptical disk; and a hardware device specially configured tostore and execute program commands, such as a read-only memory (ROM), arandom access memory (RAM), a flash memory, or the like, so that aprogram instruction may be stored therein. As an example of anothermedium, there may be an application store for distributing anapplication, a site for supplying or distributing other varioussoftware, a recordable medium or a storage medium managed in a server,or the like.

According to one embodiment, a method may be provided as being includedin a computer program product. A computer program product may beexchanged between a seller and a purchaser as a commodity. A computerprogram product may be distributed in the form of a machine-readablestorage medium (e.g., compact disc read only memory (CD-ROM)) ordistributed online through an application store (e.g. PlayStore™). Inthe case of on-line distribution, at least a portion of the computerprogram product may be stored temporarily or at least temporarily in astorage medium such as a manufacturer's server, a server of anapplication store, or a memory of a relay server.

The AI model described above can be implemented in a software module.When implemented in a software module (e.g., a program module includinginstructions), the AI model may be stored on a computer readablerecording medium.

The AI model may also be provided in the form of downloadable software.The computer program product may include a product (e.g., a downloadableapplication) in the form of a software program that is electronicallydistributed via a manufacturer or an electronic market. For electronicdistribution, at least a portion of the software program may be storedon a storage medium or may be temporarily generated. In this case, thestorage medium may be a server of a manufacturer or an electronicmarket, or a storage medium of a relay server.

Each of the elements (for example, a module or a program) according toone or more embodiments may be comprised of a single entity or aplurality of entities, and some sub-elements of the abovementionedsub-elements may be omitted, the elements may be further included invarious embodiments. Alternatively or additionally, some elements (e.g.,modules or programs) may be integrated into one entity to perform thesame or similar functions performed by each respective element prior tointegration. Operations performed by a module, program, or otherelement, in accordance with various embodiments, may be performedsequentially, in a parallel, repetitive, or heuristically manner, or atleast some operations may be performed in a different order.

While various embodiments have been illustrated and described withreference to certain drawings, the disclosure is not limited to specificembodiments or the drawings, and it will be understood by those ofordinary skill in the art that various changes in form and details maybe made therein without departing from the spirit and scope as defined,for example, by the following claims and their equivalents.

What is claimed is:
 1. An electronic apparatus comprising: a processorconfigured to perform operations comprising: obtaining first upscalinginformation of an input image by using a first artificial intelligence(AI) model, wherein the first AI model is trained to obtain upscalinginformation; obtaining a downscaled image by downscaling, based on thefirst upscaling information, the input image; and obtaining, based on anoutput resolution, an output image by upscaling the downscaled image. 2.The electronic apparatus of claim 1, wherein the first upscalinginformation comprises at least one of upscaling ratio information of theinput image or original resolution information of the input image. 3.The electronic apparatus of claim 1, wherein the processor is furtherconfigured to perform obtaining the output image by: performing firstimage quality processing of the downscaled image to obtain a secondimage, obtaining an upscaled second image by upscaling the second image,and performing second image quality processing of the upscaled secondimage.
 4. The electronic apparatus of claim 1, wherein the processor isfurther configured to perform upscaling the downscaled image by using asecond AI model, wherein the second AI model has been trained to performsuper resolution processing.
 5. The electronic apparatus of claim 4,wherein the first AI model is configured to obtain the first upscalinginformation based on feature information of the input image, and theprocessor is further configured to perform obtaining the first upscalinginformation by inputting the feature information to the second AI model.6. The electronic apparatus of claim 5, wherein the processor is furtherconfigured to perform: identifying a first edge region in the inputimage, and obtaining the feature information, wherein the featureinformation includes first feature information with respect to a firstpixel included in the first edge region.
 7. The electronic apparatus ofclaim 6, wherein the processor is further configured to perform furtheroperations comprising: blurring the first edge region, obtaining thefirst feature information, wherein the first pixel is not in a marginregion, and the margin region is set with respect to a center pixelincluded in the blurred first edge region, and obtaining the firstupscaling information by inputting the first feature information to thefirst AI model.
 8. The electronic apparatus of claim 6, wherein theprocessor is configured to perform: identifying a region of interest inthe input image, and identifying, within the region of interest, thefirst edge region.
 9. The electronic apparatus of claim 1, wherein theprocessor is further configured to perform obtaining the first upscalinginformation by inputting, to the first AI model, a second imageincluding an edge region of the input image.
 10. The electronicapparatus of claim 1, wherein the processor is further configured toperform determining whether to downscale the input image based on atiming when a scene of the input image is changed.
 11. The electronicapparatus of claim 1, wherein the first upscaling information isassociated with a first scene section of the input image, and theprocessor is further configured to perform the downscaling, based on thefirst upscaling information starting with a frame included in a secondscene section, wherein the second scene section is after the first scenesection.
 12. The electronic apparatus of claim 11, wherein the processoris further configured to perform: obtaining the first upscalinginformation in a predetermined frame interval unit of the input image,and based on second upscaling information being unchanged from the firstupscaling information in a scene section of a threshold number of scenesafter the first scene section, downscaling the input image starting fromthe second scene section, wherein the second scene section occurs afterthe threshold number of scenes.
 13. The electronic apparatus of claim 1,wherein the first AI model is trained based on first information relatedto a plurality of training images, wherein the first informationincludes at least one of a resolution, a compression method, acompression rate, a data transmission speed, an upscaling ratio,enhancement processing, or compression and scaling order is differentthan that of the input image.
 14. A method of controlling an electronicapparatus, the method comprising: obtaining first upscaling informationof an input image by using a first artificial intelligence (AI) model,wherein the first AI model is trained to obtain upscaling information;obtaining a downscaled image by downscaling, based on the firstupscaling information, the input image; and obtaining, based on anoutput resolution, an output image by upscaling the downscaled image.15. The method of claim 14, wherein the first upscaling informationcomprises at least one of upscaling ratio information of the input imageor original resolution information of the input image.
 16. The method ofclaim 14, wherein the method further comprises: performing first imagequality processing of the downscaled image to obtain a second image;upscaling the second image; and obtaining the output image by performingsecond image quality processing of the upscaled second image.
 17. Themethod of claim 14, wherein the method further comprises upscaling thedownscaled image by using a second AI model, wherein the second AI modelhas been trained to perform super resolution processing.
 18. The methodof claim 17, wherein the first AI model is configured to obtain theupscaling information based on feature information of the input image,and the obtaining the upscaling information further comprises obtainingthe first upscaling information by inputting the feature information tothe second AI model.
 19. The method of claim 14, wherein the downscalingthe input image further comprises determining whether to downscale theinput image based on a timing when a scene of the input image ischanged.
 20. The method of claim 14, wherein the first AI model istrained based on first information related to a plurality of trainingimages, wherein the first information includes at least one of aresolution, a compression method, a compression rate, a datatransmission speed, an upscaling ratio, enhancement processing, orcompression and scaling order is different than that of the input image.